Fighting Spam, Phishing and Online Scams at the Network Level

Nick FeamsterGeorgia Tech

with Anirudh Ramachandran, Shuang Hao, Nadeem Syed, Alex Gray, Sven Krasser, Santosh Vempala

2

Spam: More than Just a Nuisance

• 95% of all email traffic– Image and PDF Spam

(PDF spam ~12%)

• As of August 2007, one in every 87 emails constituted a phishing attack

• Targeted attacks on the rise– 20k-30k unique phishing attacks per month

Source: CNET (January 2008), APWG

3

Filtering

• Prevent unwanted traffic from reaching a user’s inbox by distinguishing spam from ham

• Question: What features best differentiate spam from legitimate mail?– Content-based filtering: What is in the mail?– IP address of sender: Who is the sender?– Behavioral features: How the mail is sent?

Conventional Approach: Content Filters

• Trying to hit a moving target...

...and even mp3s!

PDFs Excel sheets Images

5

Problems with Content Filtering

• Low cost to evasion: Spammers can easily alter features of an email’s content can be easily adjusted and changed

• Customized emails are easy to generate: Content-based filters need fuzzy hashes over content, etc.

• High cost to filter maintainers: Filters must be continually updated as content-changing techniques become more sophisticated

6

Another Approach: IP Addresses

• Problem: IP addresses are ephemeral

• Every day, 10% of senders are from previously unseen IP addresses

• Possible causes– Dynamic addressing– New infections

7

Idea: Network-Based Filtering

• Filter email based on how it is sent, in addition to simply what is sent.

• Network-level properties are less malleable– Set of target recipients– Hosting or upstream ISP (AS number)– Membership in a botnet (spammer, hosting

infrastructure)– Network location of sender and receiver

8

Challenges

• Understanding the network-level behavior– What behaviors do spammers have?– How well do existing techniques work?

• Building classifiers using network-level features– Key challenge: Which features to use?– Algorithms: SpamTracker and SNARE

• Building the system – Dynamism: Behavior itself can change– Scale: Lots of email messages (and spam!) out there

9

Data Collection: Spam and BGP• Spam Traps: Domains that receive only spam• BGP Monitors: Watch network-level reachability

Domain 1

Domain 2

17-Month Study: August 2004 to December 2005

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Data Collection: MailAvenger

• Highly configurable SMTP server• Collects many useful statistics

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BGP “Spectrum Agility”• Hijack IP address space using BGP• Send spam• Withdraw IP address

A small club of persistent players appears to be using

this technique.

Common short-lived prefixes and ASes

61.0.0.0/8 4678 66.0.0.0/8 2156282.0.0.0/8 8717

~ 10 minutes

Somewhere between 1-10% of all spam (some clearly intentional,

others might be flapping)

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Why Such Big Prefixes?

• Visibility: Route typically won’t be filtered (nice and short)

• Flexibility: Client IPs can be scattered throughout dark space within a large /8– Same sender usually returns with different IP

addresses

13

Characteristics of Agile Senders

• IP addresses are widely distributed across the /8 space

• IP addresses typically appear only once at our sinkhole

• Depending on which /8, 60-80% of these IP addresses were not reachable by traceroute when we spot-checked

• Some IP addresses were in allocated, albeit unannounced space

• Some AS paths associated with the routes contained reserved AS numbers

14

Other Findings

• Top senders: Korea, China, Japan– Still about 40% of spam coming from U.S.

• More than half of sender IP addresses appear less than twice

• ~90% of spam sent to traps from Windows

15

What about IP-based blacklists?

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Two Metrics

• Completeness: The fraction of spamming IP addresses that are listed in the blacklist

• Responsiveness: The time for the blacklist to list the IP address after the first occurrence of spam

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Completeness and Responsiveness

• 10-35% of spam is unlisted at the time of receipt• 8.5-20% of these IP addresses remain unlisted

even after one month

Data: Trap data from March 2007, Spamhaus from March and April 2007

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Completeness of IP Blacklists

~80% listed on average

~95% of bots listed in one or more blacklists

Number of DNSBLs listing this spammer

Only about half of the IPs spamming from short-lived BGP are listed in any blacklistF

ract

ion

of

all

spam

rec

eive

d

Spam from IP-agile senders tend to be listed in fewer blacklists

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What’s Wrong with IP Blacklists?

• Based on ephemeral identifier (IP address)– More than 10% of all spam comes from IP addresses not seen

within the past two months• Dynamic renumbering of IP addresses• Stealing of IP addresses and IP address space• Compromised machines

• IP addresses of senders have considerable churn

• Often require a human to notice/validate the behavior– Spamming is compartmentalized by domain and not analyzed

across domains

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Ephemeral: Addresses Keep ChangingF

ract

ion

of

IP A

dd

ress

es

About 10% of IP addresses never seen before in trace

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Low Volume to Each Domain

Lifetime (seconds)

Am

ou

nt

of

Sp

am

Most spammers send very little spam, regardless of how long they have been spamming.

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Where do we go from here?

• Option 1: Stronger sender identity– Stronger sender identity/authentication may make

reputation systems more effective– May require changes to hosts, routers, etc.

• Option 2: Filtering based on sender behavior– Can be done on today’s network– Identifying features may be tricky, and some may

require network-wide monitoring capabilities

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Outline

• Understanding the network-level behavior– What behaviors do spammers have?– How well do existing techniques work?

• Building classifiers using network-level features– Key challenge: Which features to use?– Algorithms: SpamTracker and SNARE

• Building the system (SpamSpotter)– Dynamism: Behavior itself can change– Scale: Lots of email messages (and spam!) out there

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SpamTracker

• Idea: Blacklist sending behavior (“Behavioral Blacklisting”)– Identify sending patterns commonly used by

spammers

• Intuition: Much more difficult for a spammer to change the technique by which mail is sent than it is to change the content

25

SpamTracker Approach

• Construct a behavioral fingerprint for each sender

• Cluster senders with similar fingerprints

• Filter new senders that map to existing clusters

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Some Patterns of Sending are Invariant

domain1.com domain2.com domain3.com

spam spam spam

IP Address: 76.17.114.xxx

DHCPReassignment

domain1.com domain2.com domain3.com

spam spam spam

IP Address: 24.99.146.xxx

• Spammer's sending pattern has not changed• IP Blacklists cannot make this connection

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SpamTracker: Identify Invariant

domain1.com domain2.com domain3.com

spam spam spam

IP Address: 76.17.114.xxxKnown Spammer

DHCPReassignment

Behavioral fingerprint

domain1.com domain2.com domain3.com

spam spam spam

IP Address: 24.99.146.xxxUnknown sender

Cluster on sending behavior

Similar fingerprint!

Cluster on sending behavior

Infection

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Building the Classifier: Clustering

• Feature: Distribution of email sending volumes across recipient domains

• Clustering Approach– Build initial seed list of bad IP addresses– For each IP address, compute feature vector:

volume per domain per time interval– Collapse into a single IP x domain matrix:– Compute clusters

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Clustering: Output and Fingerprint

• For each cluster, compute fingerprint vector:

• New IPs will be compared to this “fingerprint”

IP x IP Matrix: Intensity indicates pairwise similarity

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Classifying IP Addresses

• Given “new” IP address, build a feature vector based on its sending pattern across domains

• Compute the similarity of this sending pattern to that of each known spam cluster– Normalized dot product of the two feature vectors– Spam score is maximum similarity to any cluster

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Evaluation

• Emulate the performance of a system that could observe sending patterns across many domains– Build clusters/train on given time interval

• Evaluate classification– Relative to labeled logs– Relative to IP addresses that were eventually listed

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Data

• 30 days of Postfix logs from email hosting service– Time, remote IP, receiving domain, accept/reject– Allows us to observe sending behavior over a large

number of domains– Problem: About 15% of accepted mail is also spam

• Creates problems with validating SpamTracker

• 30 days of SpamHaus database in the month following the Postfix logs– Allows us to determine whether SpamTracker detects

some sending IPs earlier than SpamHaus

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Classification ResultsHam

Spam

SpamTracker Score

Not always so accurate!

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Early Detection Results

• Compare SpamTracker scores on “accepted” mail to the SpamHaus database– About 15% of accepted mail was later determined to

be spam– Can SpamTracker catch this?

• Of 620 emails that were accepted, but sent from IPs that were blacklisted within one month– 65 emails had a score larger than 5 (85th percentile)

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Evasion

• Problem: Malicious senders could add noise– Solution: Use smaller number of trusted domains

• Problem: Malicious senders could change sending behavior to emulate “normal” senders– Need a more robust set of features…

36

Improving Classification

• Lower overhead• Faster detection• Better robustness (i.e., to evasion, dynamism)

• Use additional features and combine for more robust classification– Temporal: interarrival times, diurnal patterns– Spatial: sending patterns of groups of senders

37

Outline

• Understanding the network-level behavior– What behaviors do spammers have?– How well do existing techniques work?

• Building classifiers using network-level features– Key challenge: Which features to use?– Algorithms: SpamTracker and SNARE

• Building the system (SpamSpotter)– Dynamism: Behavior itself can change– Scale: Lots of email messages (and spam!) out there

38

SNARE: Automated Sender Reputation

• Goal: Sender reputation from a single packet?(or at least as little information as possible)– Lower overhead– Faster classification– Less malleable

• Key challenge– What features satisfy these properties and can

distinguish spammers from legitimate senders

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Sender-Receiver Geodesic Distance

90% of legitimate messages travel 2,200 miles or less

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Density of Senders in IP Space

For spammers, k nearest senders are much closer in IP space

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Combining Features

• Put features into the RuleFit classifier• 10-fold cross validation on one day of query logs

from a large spam filtering appliance provider

• Using only network-level features• Completely automated

42

Outline

• Understanding the network-level behavior– What behaviors do spammers have?– How well do existing techniques work?

• Building classifiers using network-level features– Key challenge: Which features to use?– Algorithms: SpamTracker and SNARE

• Building the system (SpamSpotter)– Dynamism: Behavior itself can change– Scale: Lots of email messages (and spam!) out there

43

Real-Time Blacklist Deployment

• As mail arrives, lookups received at BL

• Queries provide proxy for sending behavior

• Train based on received data

• Return score

Approach

44

Challenges

• Scalability: How to collect and aggregate data, and form the signatures without imposing too much overhead?

• Dynamism: When to retrain the classifier, given that sender behavior changes?

• Reliability: How should the system be replicated to better defend against attack or failure?

• Sensor placement: Where should monitors be placed to best observe behavior/construct features?

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Design Choice: Augment DNSBL• Expressive queries

– SpamHaus: $ dig 55.102.90.62.zen.spamhaus.org

• Ans: 127.0.0.3 (=> listed in exploits block list)– SpamSpotter: $ dig \

receiver_ip.receiver_domain.sender_ip.rbl.gtnoise.net

• e.g., dig 120.1.2.3.gmail.com.-.1.1.207.130.rbl.gtnoise.net

• Ans: 127.1.3.97 (SpamSpotter score = -3.97)

• Also a source of data– Unsupervised algorithms work with unlabeled

data

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Design Choice: Sampling

Relatively small samples can achieve low false positive rates

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Sampling: Training Time

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Dynamism: Accuracy over Time

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Improvements

• Accuracy– Synthesizing multiple classifiers– Incorporating user feedback– Learning algorithms with bounded false positives

• Performance– Caching/Sharing– Streaming

• Security– Learning in adversarial environments

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Summary: Network-Based Behavioral Filtering

• Spam increasing, spammers becoming agile– Content filters are falling behind– IP-Based blacklists are evadable

• Up to 30% of spam not listed in common blacklists at receipt. ~20% remains unlisted after a month

• Complementary approach: behavioral blacklisting based on network-level features– Blacklist based on how messages are sent– SpamTracker: Spectral clustering

• catches significant amounts faster than existing blacklists– SNARE: Automated sender reputation

• ~90% accuracy of existing with lightweight features– SpamSpotter: Putting it together in an RBL system

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References• Anirudh Ramachandran and Nick Feamster, “Understanding

the Network-Level Behavior of Spammers”, ACM SIGCOMM, 2006

• Anirudh Ramachandran, Nick Feamster, and Santosh Vempala, “Filtering Spam with Behavioral Blacklisting”, ACM CCS, 2007

• Nadeem Syed, Shuang Hao, Nick Feamster, Alex Gray and Sven Krasser, “SNARE: Spatio-temporal Network-level Automatic Reputation Engine”, GT-CSE-08-02

• Anirudh Ramachandran, Shuang Hao, Hitesh Khandelwal, Nick Feamster, Santosh Vempala, “A Dynamic Reputation Service for Spotting Spammers”, GT-CS-08-09

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Additional History: Message Size Variance

Senders of legitimate mail have a much higher variance in sizes of messages they send

Message Size Range

Certain Spam

Likely Spam

Likely Ham

Certain Ham

Surprising: Including this feature (and others with more history) can actually decrease the accuracy of the classifier